Among‐population variation in drought responses is consistent across life stages but not between native and non‐native ranges
Bibliographic record
Abstract
Understanding how widespread species adapt to variation in abiotic conditions across their ranges is fundamental to ecology. Insight may come from studying how among-population variation (APV) in the common garden corresponds with the environmental conditions of source populations. However, there are no such studies comparing native vs non-native populations across multiple life stages. We examined APV in the performance and functional traits of 59 Conyza canadensis populations, in response to drought, across large aridity gradients in the native (North America) and non-native (Eurasia) ranges in three experiments. Our treatment (dry vs wet) was applied at the recruitment, juvenile, and adult life stages. We found contrasting patterns of APV in drought responses between the two ranges. In the native range, plant performance was less reduced by drought in populations from xeric than mesic habitats, but such relationship was not apparent for non-native populations. These range-specific patterns were consistent across the life stages. The weak adaptive responses of non-native populations indicate that they can become highly abundant even without complete local adaptation to abiotic environments and suggest that long-established invaders may still be evolving to the abiotic environment. These findings may explain lag times in invasions and raise concern about future expansions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".